1. Field of the Invention
The present invention relates to a digital image processing technique, and more particularly to a method and apparatus for detecting vessels in a diagnostic image.
2. Description of the Related Art
Medical problems can be diagnosed from mammography images by recognizing and detecting microcalcifications. A key problem with the aforementioned methodology is the large number of false positives (FPs) that occur in vascular regions as the sensitivity of the calcification detection algorithm is increased. An example of an FP is a vascular region mistakenly identified as a microcalcification. Such large number of FPs occurs because the calcification detection algorithm is easily confused by high frequency structure of vessels present in mammography images. An additional challenge to accurate detection and recognition of calcifications is the fact that signals generated by isolated calcifications are similar to signals generated by vessels. Since calcifications located within vessels are benign and therefore of no interest, an automated detection system that identifies calcifications in mammography images must identify vascular regions as well. An automated calcification detection system should therefore contain a module that identifies vascular regions and then rules out vascular FP finds. Additional modules in the automated detection system will identify FPs from other sources. The result is a calcification detection system that operates at high levels of precision and specificity.
The techniques currently used to detect and/or enhance the appearance of vascular images in retinal images or arteriograms (DSA) do not work for mammography. The general problem with these approaches is that the edge profiles are not always manifested with a strong contrast against the background. Furthermore, in basing edge detection solely on edge derivatives, one is highly susceptible to noise in images leading to non-robust results.
A few publications have studied automated identification of vessel contours. One such technique is described in “Automated Identification of Vessel Contours in Coronary Arteriograms by an Adaptive Tracking Algorithm”, Ying Sun, IEEE Trans. Med. Imag., Vol. 8, No. 1 (March 1989). However, with the method described in this work, one will have to manually select a starting point to begin tracking. This makes the detection process semi-automated. Secondly, the use of a matched filter in the above mentioned application to enhance the profile of vessels works only under the assumption of a known structure and scale of vessels, and a low level of image noise. This is unlikely to be the case for mammography images.
Another technique is described in “Mapping the Human Retina”, Section 6, Pinz et al., IEEE Trans. Medical Imaging, vol. 17, no. 4, pp. 606-619, 1998. In this publication a retinal map is created from scanning laser ophthalmoscope (SLO) images by finding matching edge pairs of vessels. The search range has constraints such as a minimum and maximum width, opposite gradients, and angle between edge pixel pairs. Best pixel pairs are decided based solely upon gradient. Since vessels present in mammography images have discontinuous edges and exhibit high noise, one needs to use additional constraints in edge detection.